What the data for this category looks like
Business data for communication towers and infrastructure comes from operation and maintenance ledgers, project approval documents, quarterly inspection reports, and filing materials. Updates are archived and completed after project completion. Daily inspection data is synced weekly. Most documents are in PDF, Excel, or Word format. They include structured fields such as tower number, longitude and latitude, altitude (unit: meters), inspection date, and fault level. Some Excel spreadsheets and inspection reports contain embedded tower site photos and base detection images.
What constraints these characteristics impose on document parsing and chunking
The presence of embedded site photos and detection images requires the parsing process to support extraction of non-text content and association with corresponding business text. This avoids retaining only text information. Structured fields include physical parameters with units. During chunking, the binding relationship between fields and units must be preserved. This prevents parameters from losing business meaning after splitting. Documents have diverse formats, and some content is process-based text. Chunk boundaries must match the node logic of business handling. This avoids splitting approval steps. Incrementally updated inspection data must be associated with the context of historical archived documents. Chunking rules must adapt to update frequency characteristics. This ensures traceability of knowledge base content.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Communication tower documents contain structured parameters and process descriptions. This range balances parameter completeness and contextual coherence |
chunkSplitter | Split by paragraph and table boundaries | Inspection tables and process text exist in documents. Splitting by boundaries preserves business logic integrity |
customPdfParse | Enabled | Embedded tower site photos in PDFs must be extracted and associated with corresponding text content |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some archived project filing documents have large file sizes. This setting adapts to single-file upload limits |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large documents with multiple embedded images take longer to parse. Extending the timeout threshold prevents parsing interruptions |
chunkSettingMode | Manual configuration | Chunking rules must be adjusted for structured fields and image association scenarios. This adapts to the custom configuration logic of FastGPT v4.17.0 |
Three Common Misconfigurations
- The parsed Excel document only displays cell text, and embedded tower base photos are not extracted. The root cause is that custom parsing configuration for embedded images is not enabled, and only plain text content is extracted.
- Parsing tasks return a timeout status code, and large project filing PDF parsing tasks fail. The root cause is that the
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and document parsing time exceeds the default threshold. - Approval steps of the business handling process are split into two chunks, and cannot fully match the handling logic. The root cause is that chunking rules only split by fixed character count, and do not match process node boundaries.
How to Verify Proper Configuration
- Upload a single Excel inspection document with embedded images. Check if the parsing result includes text descriptions associated with images and image pending verification identifiers.
- Upload a single large project filing PDF. Confirm that the parsing task does not trigger a timeout interruption.
- Select a document fragment containing structured parameters. Verify that the chunking result does not split the binding relationship between fields and their corresponding units.
- Submit incrementally updated inspection data documents. Confirm that chunks of new content can be correctly associated with historical context.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.